215 results about "Risk impact" patented technology

The invention provides an enterprise data risk processing method and system based on a dynamic knowledge graph, and the method comprises the steps: collecting a real-time data flow generated in the operation process of an enterprise, extracting an entity object and an incidence relation in the real-time data flow, and forming an initial node and an initial edge; and receiving a risk processing request, positioning an initial node corresponding to the target entity from the dynamic knowledge graph according to the risk attribute identifier, and determining an association propagation path generation rule of the target entity based on the risk influence range description. And calling a pre-trained risk propagation model, traversing adjacent nodes in the dynamic knowledge graph according to a correlation propagation path generation rule by taking the initial node as a starting point, and generating a risk propagation path set corresponding to the target entity. And calculating a comprehensive risk value corresponding to the target entity according to the node connection density and the risk attribute superposition intensity of each risk propagation path, and triggering a risk early warning signal when the comprehensive risk value exceeds a preset threshold. The enterprise risk prevention and control initiative and decision-making accuracy can be improved.

The invention discloses a digital twinning-based major engineering construction safety active control method, system and equipment, and belongs to the technical field of construction active control. The problems of low safety risk identification efficiency and inaccurate risk control means in the major engineering construction process are solved, major engineering construction site environment and structural feature data are obtained through space-air-ground-water multi-way intelligent means and equipment, a digital twinborn model of a real-time mapping construction site is established, and the safety risk identification efficiency of the major engineering construction site is improved. Correcting the digital twinborn model by using real-time data; a digital twinborn model is used as a basic model of finite element analysis, mechanical properties of different construction stages are analyzed, dynamic early warning is performed on an analysis result in combination with real-time data, a safety risk derivation model is established to perform real-time analysis on key problems such as a risk point location, a risk influence range and risk derivation prediction in the digital twinborn model, and the safety risk derivation prediction result is obtained. Data support is provided for engineering safety active control, an active control strategy model is established, a control strategy is output to a servo control device according to risk information, strategy dynamic adjustment is performed according to a real-time effect, and high-precision cooperative control of major engineering safety monitoring is realized.

The invention relates to the technical field of risk analysis, in particular to a supply chain risk quantitative evaluation method and system based on a dynamic affair atlas, and the method comprises the steps: collecting multi-source heterogeneous data, constructing a four-dimensional space-time model comprising a time dimension, a geographic space dimension, a supply chain network space dimension and a risk influence space dimension, representing the supply chain event as four-dimensional spatio-temporal data; a supply chain entity is identified from the four-dimensional spatio-temporal data, risk events are extracted, a affair graph is constructed, and the affair graph takes the risk events as nodes and the evolution relation between the events as edges to calculate the relation weight between the events; calculating a probability quantized value of the risk conduction path based on the affair map, and obtaining a comprehensive risk score of the target entity; generating a risk mitigation strategy based on the comprehensive risk score; and monitoring the deviation between the actual risk occurrence condition and the prediction result, and updating the affair map and the risk mitigation strategy through adaptive parameter optimization and an incremental learning mechanism to form a self-evolutionary risk assessment system.

The invention relates to the technical field of meteorological engineering, and discloses a meteorological engineering project management risk assessment method, which comprises the following steps of: firstly, collecting historical risk event data of a meteorological engineering project and related meteorological conditions, engineering parameters and management measures, and performing data cleaning and integration to form a standardized risk data set; and based on the data set, constructing a risk prediction model, and predicting types and probabilities of risks possibly encountered by the project in the future. Meanwhile, a risk influence assessment system is established in combination with historical data, and the risk is comprehensively assessed from three dimensions of risk severity, occurrence possibility and controllability. The risk prediction and the assessment system are combined, the risk comprehensive influence value is calculated, then the risks are sorted and graded, and coping strategies are made for high-risk and medium-risk links. And finally, the risk assessment result and strategy are presented in a visual report form, comprehensive risk assessment support is provided for project management personnel, and the risk prevention and control capability of meteorological engineering project management is effectively improved.

The invention relates to a risk analysis method, and particularly provides a dynamic risk analysis method based on a Bayesian network. Converting the risk influence factors into a first group of network nodes based on a historical risk knowledge model, and constructing an initial topological structure according to a knowledge model logic relationship; secondly, acquiring multi-source real-time monitoring data, learning correlation among variables through a data mining algorithm, and generating a second group of nodes and a data-driven topological structure; then, connecting the historical information Bayesian network with the data-driven Bayesian network through a shared risk node, and constructing a comprehensive Bayesian network; meanwhile, a dynamic probability updating mechanism is established, and comprehensive network probability parameters are dynamically adjusted by adopting a weighted fusion algorithm in combination with a historical prior probability and a real-time posterior probability; and finally, performing risk reasoning based on the dynamically updated integrated network, and outputting risk quantitative indexes.

The invention relates to the technical field of data security, and discloses a data security risk assessment method based on model construction, and the method comprises the steps: firstly collecting historical data security events and related system configuration, network architecture and other multi-dimensional data, and carrying out the preprocessing to form a standardized data set; and then, constructing a data security risk prediction model, and predicting future possible data security events and potential influences thereof in combination with multi-dimensional factors. And obtaining a risk prediction result by obtaining data such as a system state and network flow in real time and inputting the data into the prediction model. Meanwhile, a risk influence assessment framework is established, the severity, occurrence frequency and business influence of the risk are comprehensively considered, and a risk comprehensive score is calculated. And finally, sorting and classifying the risks according to the scores, and formulating and implementing corresponding risk relief measures and emergency response plans for high-risk and medium-risk data assets. According to the invention, the accuracy and timeliness of data security risk assessment are improved.

The invention discloses a harbor district accident risk visualization analysis method based on a network topology structure, and the method comprises the steps: taking a key facility and an important region as network nodes in a harbor district, determining a connection relation to obtain a network topology structure, analyzing historical cases to obtain a determined risk factor set, obtaining an uncertain factor set through edge calculation, and carrying out the calculation of the uncertain factor set. The method comprises the following steps: constructing an accident tree to carry out quantitative evaluation on network nodes and connection risks, constructing a risk evolution model, extracting an accident cause chain and calculating a risk influence range, visualizing a network topology structure, carrying out color mapping on the accident cause chain and the risk influence range, and then adding the accident cause chain and the risk influence range to obtain a three-dimensional topology view. According to the method, the network topology structure visualization analysis of the accident risk of the harbor district can be realized, the risk is comprehensively evaluated, the subsequent management decision of the harbor district is optimized, and meanwhile, the method has relatively good interpretability.

The invention discloses a financial risk control method and system, and relates to the technical field of enterprise financial management. The method comprises the following steps: acquiring related data; the related data comprises financial data, market data, operation data and external data; wherein the external data comprises an industry report, competitive product data and policy and regulation change data; constructing a knowledge graph according to the related data; the knowledge graph comprises financial risk types and corresponding risk levels and risk influence factors; based on the knowledge graph, constructing a financial risk prediction model by using a credit risk model and a quantification algorithm; the credit risk model adopts a KMV model; the quantization algorithm comprises a historical simulation method and a Monte Carlo simulation method; and performing real-time monitoring on a target enterprise by using the financial risk prediction model, and performing visual display on a prediction result in a set period. According to the invention, the risk monitoring accuracy and timeliness can be improved.

The invention relates to the technical field of artificial intelligence, in particular to an AI-based big data risk assessment system, which comprises the following modules: a time offset correction module, a risk factor modeling module, a mutation factor monitoring module, a risk cross derivation module and a prediction optimization adjustment module. According to the method, the time density of multi-source data is aligned, a reference baseline of time series data is unified, the time consistency of subsequent factor modeling is improved, the identification capability of a linkage effect between factors is enhanced by combining the trend deviation rate and co-occurrence progressive relation of risk factors, and judgment is performed by utilizing the trend deviation degree and residual error continuity of an intersection point; risk wave and range identification under the multivariable effect is optimized, a prediction error deviation trend is combined with a weight self-adjusting mechanism, dynamic correction of model parameters is achieved, it is ensured that the model adapts to dynamic changes of the risk environment in the long-term operation process, a risk prediction path has the self-adaptive repairing capacity, and the risk prediction accuracy is improved. And the stability and accuracy of big data risk prediction are improved.

The invention provides a supply chain toughness and efficiency collaborative improvement method and system and electronic equipment, and the method comprises the steps: obtaining various supply chain management variable data of a supply chain, such as an inventory level, a supplier attribute, a customer order, transportation time, cost, a risk impact resistance coefficient, a recovery coefficient and the like; then, on the basis of the supply chain management variable data, various to-be-evaluated supply chain strategies meeting one or more constraint conditions of a preset continuous supply constraint condition, a preset factory production capacity constraint condition and a preset order fulfillment constraint condition are screened out, and a supply chain comprehensive target value is obtained through a calculation formula of the supply chain comprehensive target value. And outputting the optimal supply chain strategy with the maximum supply chain comprehensive target value. According to the embodiment of the invention, the optimal supply chain strategy which can guarantee the efficiency of the supply chain, can resist risk impact and can be quickly recovered can be determined, the comprehensive performance of the supply chain is further improved, and the sustainable development of an enterprise is maintained.

The invention relates to the technical field of supply chain risk assessment, in particular to a supply chain risk assessment system based on multi-source data fusion, which comprises a data acquisition module, a configuration multi-source data acquisition end and establishment of a binding relationship between the acquisition end and each node in a database, and is characterized in that the database is used for storing multi-source data related to supply chain risk assessment; the data fusion module is used for constructing a knowledge graph based on the multi-source data, nodes of the knowledge graph correspond to risk influence factors, and edges of the knowledge graph represent the incidence relation between the factors; the model construction module is used for constructing a risk assessment model based on the knowledge graph; the risk assessment module performs risk assessment on the supply chain based on the risk assessment model to obtain a risk assessment result; by fusing the multi-source data, the change condition of the multi-source data is monitored in real time, the dynamic change in the supply chain is responded in real time, the evaluation strategy is adjusted in time, and the accuracy and timeliness of the evaluation result are improved.

The invention discloses a bidding offer file auditing method based on a power transmission and transformation project, and relates to the technical field of file auditing, and the method comprises the following steps: obtaining a bidding offer file, carrying out the preliminary auditing, and recognizing the risk data in a project; establishing an index system based on the risk data and a preset index construction principle; building a project bidding quotation risk assessment model through an analytic hierarchy process and a fuzzy comprehensive evaluation method based on the index system; and applying a to-be-examined file to the project bidding quotation risk assessment model to obtain a risk level of the project, and if the risk level is greater than a preset bearable risk level, proposing an improvement suggestion and adjusting a scheme. According to the invention, by analyzing and evaluating influence factors, effective measures are taken in time for response, risk influence is eliminated or weakened, and the risk management capability of the system is improved, so that the quotation is more advantageous, and the probability of winning bids is increased.

The invention discloses a project cost risk control management system, which relates to the technical field of risk management and control and comprises a potential risk analysis module, a risk assessment unit, a risk probability assessment module, a risk influence assessment module, a risk level assessment module, a risk response module, an early warning terminal and a database. By integrating multi-dimensional data and historical project data and combining industry risk indexes and supplier stability scores, the scientificity and objectivity of evaluation are enhanced, a quantitative basis is provided for risk management decision, and in risk influence evaluation, through risk simulation and multi-index analysis, the risk influence evaluation efficiency is improved. According to the method, the influence of potential risks on project cost, construction period and technical performance is comprehensively evaluated, comprehensive data support is provided for making risk coping strategies, and in the risk level evaluation process, through comprehensive analysis of risk probabilities and influences, scientific risk level division and priority ranking are facilitated.

The invention discloses an IDI full-project cycle risk digital assessment method and system, which have the beneficial effects that potential risk factors are identified from project data by adopting a text mining technology, risk factor importance assessment is carried out by utilizing a mode of weighting word frequency through information entropy, risk influence factors can be comprehensively identified, and the risk assessment accuracy is improved. Exploring the internal influence of project quality and risk factors; a multi-level and multi-dimensional risk evaluation system is constructed by adopting an analytic hierarchy process, so that the quality risk state of a project can be comprehensively reflected, and a quantitative standard of an evaluation index system is given; according to the method, a project risk evaluation model is constructed, a project risk scoring model is constructed from four dimensions of a hierarchical weight matrix, a severity matrix, a conditional probability matrix and an index occurrence probability matrix of risk indexes, various risk factors are considered, and the mutual relation and occurrence possibility between risks can be known more comprehensively; a Monte Carlo simulation method is adopted to grade and divide project risks, and the risk can be quickly traced, monitored and managed according to a result.

The invention discloses a big data intelligent analysis method and system based on artificial intelligence, and belongs to the field of electric power analysis. Equipment risk assessment is performed through the acquired environment change data in combination with equipment anomaly analysis, equipment risk influence assessment is performed based on an equipment risk assessment result and an equipment position, and early warning of an equipment risk level is performed based on an equipment risk influence assessment result. According to the method, the operation of the substation equipment is comprehensively evaluated through the operation data and the aging data of the equipment, then the operation stability of the equipment in the future substation is evaluated according to the environment change condition, and appropriate suggestions are made for replacement or maintenance of the equipment under the influence of the environment. And the stability and the safety of the transformer substation under the influence of the environment are further improved.

The invention relates to the technical field of supply chain management, in particular to a risk control method and system for a supply chain. According to the method, the limitation of a traditional method in processing complex data can be overcome, the inventory-production data of each link of the supply chain is acquired in real time, the risk influence index is dynamically calculated in combination with internal and external influence factors, potential risks can be comprehensively identified and dynamically assessed, and the risk assessment efficiency is improved. According to the method, coping strategies can be adjusted in time according to market demand fluctuation, policy change and other external factors, the response capability and adaptability of the supply chain are improved, the effectiveness of supply chain risk control is ensured through real-time monitoring of the risk control deviation degree, the defect that in-time early warning and adjustment are lacked in a traditional method is overcome, and the risk control efficiency is improved. Serious consequences of supply chain interruption, cost increase and the like are effectively prevented, the accuracy, the flexibility and the predictability of supply chain risk management are improved, and the supply chain risk management method has remarkable practical application value.

The invention provides a multi-dimensional risk factor index identification method based on an artificial intelligence technology, and belongs to the technical field of safety production. The method comprises the following steps: firstly, determining the influence of risk factors on the overall security risk, and forming a multi-dimensional risk matrix and a multi-dimensional risk factor dynamic time sequence model; secondly, in combination with the multi-dimensional risk matrix and the multi-dimensional risk factor dynamic time sequence model, constructing a multi-dimensional risk factor index group and an operation risk prediction model; according to the method, automation and informatization of risk factor index identification can be further improved, digitization and intelligentization are realized, the production efficiency is greatly improved, and the safety risk is reduced.

The invention relates to the technical field of traffic transportation engineering, in particular to a method and system for constructing a high-speed railway line risk map by considering transportation efficiency loss, which comprises the following steps: constructing a train operation risk event type judgment model, and classifying and identifying station and interval risk events; high-speed rail line transportation efficiency loss is quantified, a calculation model is constructed, risk event probability analysis is carried out based on risk event type judgment and efficiency loss quantification, and space-time grid potential risk influences are measured and calculated; and drawing a line transportation risk map according to the quantized value, and analyzing influence factors. According to the method, various station and interval risk events are effectively extracted and judged, various risk influences are scientifically quantified by innovatively providing the instantaneous transportation loss rate and the line transportation loss rate, accurate mastering of the risk map on the severity level and the influence range of the risk events is improved, and the risk assessment accuracy is improved. Compared with the existing research, the time-space change and distribution condition of the risk influence of each level can be more intuitively mastered.

The invention provides an electric power system asset risk prediction method and system based on big data analysis, and the method comprises the steps: carrying out the correlation analysis of all data in an electric power data set, and determining the potential correlation between an equipment operation state and environment meteorological data; analyzing the risk influence degree of the environmental meteorological factors on the power system assets according to the potential association relationship, and determining key risk factors; constructing a risk scene based on the key risk factors; power system asset operation is simulated in each risk scene, and the risk level of each risk scene is determined based on the probability of power system asset failure in each risk scene and the interference influence degree of the power system asset failure on the power grid system; and matching the environmental meteorological data of the power system assets monitored in real time with the risk scene, and determining the risk level of the power system assets having a fault. According to the invention, the comprehensiveness and accuracy of asset risk prediction are improved.

The invention provides an interaction method and system for thromboembolism risk assessment, and can be applied to the technical field of artificial intelligence. The method comprises the steps that a first server side responds to an inquiry request sent by a first terminal, and inquiry diagnosis data and historical clinical data are sent to a second server side; the second server obtains an initial risk assessment score according to the inquiry diagnosis data and the historical clinical data; under the condition that the initial risk assessment score is greater than a preset risk threshold value, the second server side sends inquiry diagnosis data and historical clinical data to a second terminal; the second terminal determines risk influence factors according to the inquiry diagnosis data and the historical clinical data; the second server obtains the detection result, and performs hospital thromboembolism risk assessment on the detection data by using a Bayesian network to obtain a target risk assessment result; and the second server determines a rehabilitation guidance strategy according to the target risk assessment result and the discharge diagnosis result, and sends the rehabilitation guidance strategy to the first terminal.

The invention provides a level protection-oriented network security configuration checking method, which comprises the following steps of: constructing a knowledge graph associated with a security control item and a technical implementation mode by relying on a standard document and configuration data, and implementing a control item logic dependency and semantic weight reconstruction model; security configuration of assets is automatically collected and semantically mapped to map nodes, configuration deviation is recognized, a risk influence path template is matched in combination with asset importance and service context, semantic propagation path influence intensity is dynamically calculated, and an accumulated risk influence value of configuration deviation is generated in an aggregation mode; according to the method, risk level judgment is carried out by adopting a piecewise nonlinear function, a structured risk assessment record is output, risk identification and reinforcement suggestion closed loop are realized, and the hierarchical management and control and automatic compliance response capability of security configuration deviation in a network environment is improved.

The invention provides an engineering safety management method and system based on a BIM model, and relates to the technical field, and the method comprises the steps: obtaining engineering information, constructing an engineering BIM model, carrying out the monitoring region division of the engineering BIM model, obtaining an engineering model monitoring region, and obtaining the region group association information; calculating an area scheme influence coefficient to obtain a risk area, and calculating a type influence coefficient of each engineering monitoring type to obtain a risk type; calculating an engineering cumulative risk influence coefficient, comparing the engineering cumulative risk influence coefficient with a preset cumulative influence threshold value, obtaining cumulative risk judgment of the engineering BIM model, and further obtaining risk influence early warning information; according to the risk early warning information, the abnormal risk monitoring type and the risk area information, reversely obtaining the wind direction source information, and adjusting the risk source information until the risk early warning information does not exist, the method can comprehensively and accurately identify and evaluate the potential risk in the project, and realizes real-time monitoring and effective early warning.

The invention discloses a project combination adjusting method, and particularly relates to a project combination adjusting method and device based on dynamic risks and electronic equipment. The method comprises the following steps: acquiring support degree information corresponding to an existing project combination at a moment t-1, and acquiring risk influence information at a moment t; updating the support degree information based on the risk influence information to obtain updated support degree information at the moment t; judging whether the existing project combination meets a preset constraint condition or not based on the updated support degree information at the moment t; under the condition that the existing project combination does not meet the preset constraint condition, the adjustment cost corresponding to each project adjustment scheme is calculated, and the project adjustment scheme comprises an adjustment strategy executed by each existing project; and adjusting the existing project combination based on the project adjustment scheme with the lowest adjustment cost to obtain an updated project combination at the moment t. According to the method, the project combination can be dynamically adjusted according to the risk impact, and efficient allocation of resources is realized.

The invention discloses a wharf risk assessment and management method based on artificial intelligence, particularly relates to the field of wharf risk assessment and management, and is used for solving the problems that an existing wharf is difficult to quantify and delay influence on a supply chain network level, and prospective intervention cannot be performed on supply chain risks. According to the method, a comprehensive data platform of wharf operation and supply chain cargo linkage is constructed, and the bottleneck time point of a future delivery window is analyzed and predicted based on the ship arrival density, the operation bearing capacity and the stockpiling structure; searching a historical supply chain interruption event and extracting corresponding cargo and node information; tracking a conduction path of an interruption event in the supply chain network, quantifying the influence of a bottleneck on a downstream node, and establishing a supply chain risk influence model; generating a supply chain risk influence map in combination with future bottleneck prediction; and finally, a composite risk portrait is constructed, and targeted risk intervention is carried out on the wharf and the supply chain node. And intelligent prediction and management of wharf and supply chain full-chain risks are realized.

The invention discloses a user big data processing method based on supply chain finance, and the method comprises the steps: collecting user interaction data, extracting short-term time sequence and long-term behavior features to construct an interaction data network, selecting risk-related features, initializing the weight, and carrying out the training adjustment; calculating a user preference attenuation coefficient according to a transaction time interval, segmenting behavior characteristics, identifying behavior and preference changes of each stage, evaluating risk influences and adjusting weights; constructing an interaction data operation chain based on the data network, and adjusting behavior change rates of the user in different stages; and finally, constructing a risk conduction rule and marshalling according to historical risk characteristics, and determining a key behavior change rate. Through multi-dimensional feature analysis and behavior change rate calculation, user dynamics are accurately described, potential risk association is mined, an evaluation model is constructed in combination with historical data, risk signals can be captured in time, risk identification accuracy and timeliness are improved, and platform risk management is assisted.

The invention provides a small hydropower station safety and ecological multi-source risk dynamic assessment method and system, and belongs to the technical field of hydropower station management. Constructing a multi-dimensional risk feature tensor, including preprocessing multi-source data to obtain standardized data, extracting features from the standardized data, organizing the features according to risk types, time, space and feature four-dimensional organizations to form a four-order tensor, and performing decomposition to extract a core risk mode; constructing a conditional risk probability field, including conditional probability density estimation, risk probability field dynamic evolution model establishment and multi-source information entropy weight fusion; constructing a risk degree assessment model, including risk influence degree matrix construction, matrix spectrum analysis and risk measurement generation; according to the method, the defects of single dimension, static evaluation, high subjectivity and the like in the prior art are overcome, and accurate quantification and dynamic evaluation of the safety and ecological risk of the small hydropower station are realized.

The invention relates to the technical field of food safety risk management, in particular to an aquatic product risk analysis system based on multi-factor analysis, which comprises a pollution factor response module, a path aggregation module, a channel decision module, a pollution risk simulation module and a risk mapping collection module. According to the method, by extracting the concentration change direction of the pollution factor in the specific water area in the continuous time period and establishing the sequence combination of the concentration rising section, the time sequence behavior characteristics of the pollution factor can be identified, and the overlapping relation between the risk factor and the biological class group exposure can be effectively judged; in a standard exposure period, contact frequencies are integrated, sensitive time periods are screened, and generality factor grouping is completed by analyzing frequency coincidence conditions of a plurality of aquatic products under the same factors, so that a risk influence map can reflect a factor interaction correlation and category common inductance response relationship; and the comprehensiveness, granularity and prediction capability of risk assessment are improved in a dynamic scene.

The invention discloses a power transmission line local forest fire tripping risk assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the position information of a power transmission line in a research region, and dividing the research region into a plurality of line grids and non-line grids; randomly selecting a certain line grid as a target grid; obtaining forest fire risk influence factors, wherein the influence factors comprise meteorological conditions, geographic factors and combustible conditions; determining a forest fire occurrence risk grade of the target line grid according to the forest fire risk influence factor; determining the flame height of the target grid according to the forest fire risk influence factor influencing the grid, and generating a corresponding flame height risk grade; and determining the forest fire tripping risk of the target line grid in combination with the forest fire occurrence risk level and the flame height risk level. By implementing the method, meteorological conditions, geographic factors and combustible conditions can be comprehensively considered, and the local forest fire tripping risk of the power transmission line can be accurately evaluated.

The invention discloses a big data electronic archive management method, and relates to the technical field of electronic archive management, and the method comprises the following components: S1, data collection and integration, S2, risk factor recognition model construction, S3, real-time risk monitoring and analysis, S4, intelligent early warning and risk assessment report generation, and S5, coping suggestion generation and pushing. According to the method, related data of the electronic archives are collected in real time, the risk factor identification model is constructed, and the data leakage risk, the storage equipment fault risk and the law compliance risk can be monitored and analyzed in real time, so that archive management personnel can be helped to discover potential safety hazards in time, and the safety of the archives is improved. In addition, the method can accurately assess the severity of the risk by quantifying the risk influence factor, so that the archive management personnel can quickly take corresponding measures to avoid or reduce the loss caused by the risk, and in addition, the method can intelligently generate a risk assessment report to provide decision support for risk prevention and control for the archive management personnel, thereby improving the risk management efficiency. And the risk prevention and control capability of electronic archive management is further improved.

The invention discloses a field operation video monitoring risk prevention and control method, system and device and a medium, and the method comprises the steps: analyzing the reference operation risks of the whole network over the years, and constructing a reference operation risk library according to the analysis result; establishing a first risk model through the reference operation risk library, and identifying risk influence factors; constructing a second risk model through the risk influence factors, determining weight values of the risk influence factors, outputting risk values, and performing risk grade division according to the risk values; deploying video monitoring equipment to identify operating personnel, synchronously monitoring an operation site, and analyzing and judging an abnormal condition through a pixel difference value of front and back frame images; and when the abnormal situation is judged, determining an early warning level in combination with the risk level, and giving an alarm. According to the method, historical risk data analysis and an intelligent video monitoring technology are deeply fused, so that risk assessment is fundamentally converted from static experience judgment to dynamic quantitative prediction.